
Learning Relations (LR) Layer: This layer seeks and learns potential relations between all input feature vectors via a convolution kernel followed by a batch normalization operator. A max pooling operator is then applied to the outputs to obtain the global feature of the input features. The max pooling is a symmetric function that guarantees that the extracted features are permutation-invariant, as stated by Property i above.
The combination of the convolution kernel, batch normalization and max pooling operators is often called multi-layer perceptron (MLP) [1]}.

